While HARP exploits specificity of quasiperiodic t-MRI, the optical flow (OF) based method is generic and can be applied to track objects in video sequences [1]}, [2]}, [3]}, [4]}, [5]}.
OF can estimate a dense motion field based on the basic assumption of image brightness constancy of local time-varying image regions with motion, at least for a very short time interval. The under-determined OF constraint equation is solved by variational principles in which some other regularization constraints are added in, including the image gradient, the phase or block matching. Although efforts have been made to seek more accurate regularization terms, OF approaches lack accuracy, especially for t-MRI motion tracking, due to the tag fading and large deformation problems [6]}, [7]}. More recently, convolutional neural networks (CNN) are trained to predict OF [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}. However, most of these works were supervised methods, with the need of a ground truth OF for training, which is nearly impossible to obtain for medical images.
